Earticle

초록

영어

Pedestrian detection is one of the hot research problems in computer vision field. The Cascade AdaBoost System is a commonly used algorithm in this region. However, when the training datasets become larger, it is still a time consuming process to build one Adaboost classifier. In this paper we detail an implementation of the AdaBoost algorithm using the NVIDIA CUDA framework based on the haar features as feature vectors, and downscaling with integral image. The result shows that we can get nearly 6x from the standard code to with our CPU implementation to achieve a near real-time performance and ensure better classification results in misjudgment.

목차

Abstract
 1. Introduction
 2. Feature Selection
 3. Adaboost Algorithm
 4. Achieve Adaboost Algorithm
 5. Experiment and Result
 6. Conclusions
 References

키워드

저자

• Chong Chao Cai [ School of Computer Engineering and Science, Shanghai University, shanghai, China, Faculty of Information Technology, Huzhou Vocational & Technical College, Huzhou, Zhejiang, China ]
• Jue Gao [ Computing Center, Shanghai University, Shanghai, China ]
• Bian Minjie [ School of Computer Engineering and Science, Shanghai University, shanghai, China, Shanghai Shang Da Hai Run Information System Co., Ltd, Shanghai, China ]
• Peicheng Zhang [ School of Computer Engineering and Science, Shanghai University, shanghai, China, Shanghai Shang Da Hai Run Information System Co., Ltd, Shanghai, China ]
• Honghao Gao [ School of Computer Engineering and Science, Shanghai University, shanghai, China, Computing Center, Shanghai University, Shanghai, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605